diff --git a/caption.py b/caption.py
index 8ca071905..eee8f328f 100644
--- a/caption.py
+++ b/caption.py
@@ -104,7 +104,7 @@ def caption_image_beam_search(encoder, decoder, image_path, word_map, beam_size=
             top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True)  # (s)
 
         # Convert unrolled indices to actual indices of scores
-        prev_word_inds = top_k_words / vocab_size  # (s)
+        prev_word_inds = torch.div(top_k_words, vocab_size, rounding_mode='floor')  # (s)
         next_word_inds = top_k_words % vocab_size  # (s)
 
         # Add new words to sequences, alphas
@@ -167,7 +167,7 @@ def visualize_att(image_path, seq, alphas, rev_word_map, smooth=True):
     for t in range(len(words)):
         if t > 50:
             break
-        plt.subplot(np.ceil(len(words) / 5.), 5, t + 1)
+        plt.subplot(int(np.ceil(len(words) / 5.)), 5, t + 1)
 
         plt.text(0, 1, '%s' % (words[t]), color='black', backgroundcolor='white', fontsize=12)
         plt.imshow(image)
diff --git a/eval.py b/eval.py
index 3e9359a09..52801f2b0 100644
--- a/eval.py
+++ b/eval.py
@@ -121,7 +121,7 @@ def evaluate(beam_size):
                 top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True)  # (s)
 
             # Convert unrolled indices to actual indices of scores
-            prev_word_inds = top_k_words / vocab_size  # (s)
+            prev_word_inds = torch.div(top_k_words, vocab_size, rounding_mode='floor')  # (s)
             next_word_inds = top_k_words % vocab_size  # (s)
 
             # Add new words to sequences